Notebooks

Video on Setting up DFFML for Machine Learning Tasks

• Moving Between Models
  • Import Packages
  • Build our Dataset
  • Instantiate our Models with parameters
  • Train our Models
  • Test our Models
  • Predict using our Models
• Evaluating Model Performance
  • Import Packages
  • Build our Dataset
  • Instantiate our Models with parameters
  • Train our Models
  • Test our Models
• Tuning Models
  • Import Packages
  • Build our Dataset
  • Instantiate our Models with parameters
  • Train our Models
  • Test our Models
  • Tuning Models
• Saving and loading models
  • Import Packages
  • Build our Dataset
  • Instantiate our Models with parameters
  • Train our Models
  • Test our Models
  • Restart kernel and load the model
  • Import Packages
  • Build our Dataset
  • Load our saved model
• Ensemble by stacking
  • Import Packages
  • Build our Dataset
  • Split our data
    • Let the Ensemble begin!
    • 1. Training First-Level Base Models on train data.
• Transfer Learning
  • Setting-up
  • Feature Extraction:
    • Define our Additional layers
    • Instantiate our Model with parameters
    • Train the Model
    • Test the Model
    • Make predictions using our Model.
• Working with Multi-Output models
  • Multi-Output Regression
    • Build our Dataset
    • Instantiate our Model with parameters
    • Train our Model
    • Test our Models
    • Make predictions using our Model.
  • Multi-Output Classifier
    • Build our Dataset
    • Instantiate our Model with parameters
    • Train our Model
    • Test our Models
    • Make predictions using our Model.